Coffee drinks prepared from moldy coffee beans can adversely affect human health. No convenient screening method for detecting the smell of stale coffee beans exists. Accordingly, this study developed an electronic nose (E-nose) system for detecting the smell of coffee beans. This system comprises an environmental control system, a sensor array, and a data signal readout system. The system can distinguish various degrees of mold on coffee beans through the recognition of the smell of the coffee beans. In this study, we established a standard operating procedure to collect gas samples from coffee beans in a temperature- and humidity-controlled environment and recorded changes in the signals by using the sensor array after introducing the target gas. Features were first extracted from the collected data, then dimensionality reduction methods, such as principal component analysis and linear discriminant analysis, were applied to these features. Thus, their complexity was reduced, and the noise was eliminated. K-nearest neighbor and support vector machine were adopted as classification algorithms, and the classification accuracy of the proposed system reached 91.77%.

 Non-maximum suppression (NMS) is an essential part of the post-processing of object detectors. Most object detection models require NMS algorithms to filter overlapping candidate boxes belonging to the same object and reserve the box that best represents the object as the object representative box. However, the hardware for the standard NMS algorithm has some disadvantages, such as high complexity (especially when box numbers are significant), high latency, large area, and high power consumption. To solve these problems, we propose an efficient parallel hardware architecture, which uses a new sorting circuit with a ping-pong buffer and a new retention mechanism of the candidate box for the new NMS algorithm called distance over side-NMS (DoS-NMS). This architecture uses a PE group with a voting mechanism to simplify the algorithm for reducing the latency and area. Additionally, the PE group computes the DoS (non-intersection over union) of the candidate box and multiple object representatives in parallel, significantly reducing algorithm complexity and memory access cost. Experiments indicated that the algorithm runs on the chip with area of 0.75mm2 , power consumption of 68.41 mW, and normalized area efficiency that is 3.72 and 4.84 times higher than the two state-of-the-art methods, respectively.